Advertisement

eHealth 360° pp 376-383 | Cite as

Data Mining of Intervention for Children with Autism Spectrum Disorder

  • Pratibha VellankiEmail author
  • Thi Duong
  • Dinh Phung
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 181)

Abstract

Studying progress in children with autism spectrum disorder (ASD) is invaluable to therapists and medical practitioners to further the understanding of learning styles and lay a foundation for building personalised intervention programs. We use data of 283 children from an iPad based comprehensive intervention program for children with ASD. Entry profiles - based on characteristics of the children before the onset of intervention, and performance profiles - based on performance of the children on the intervention, are crucial to understanding the progress of the child. We present a novel approach toward this data by using mixed-variate restricted Boltzmann machine to discover entry and performance profiles for children with ASD. We then use these profiles to map the progress of the children. Our study is an attempt to address the dataset size and problem of mining and analysis in the field of ASD. The novelty lies in its approach to analysis and findings relevant to ASD.

Keywords

Autism Spectrum Disorder Autism Spectrum Disorder Performance Profile Restricted Boltzmann Machine Missing Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5\(^{\textregistered }\)). American Psychiatric Pub (2013)Google Scholar
  2. 2.
    Lovaas, O.: Behavioral treatment and normal educational and intellectual functioning in young autistic children. J. Consult. Clin. Psychol. 55(1), 3–9 (1987)CrossRefGoogle Scholar
  3. 3.
    Dawson, G., et al.: Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev. Psychopathol. 20(3), 775 (2008)CrossRefGoogle Scholar
  4. 4.
    Hetzroni, O., Tannous, J.: Effects of a computer-based intervention program on the communicative functions of children with autism. J. Autism Dev. Disord. 34(2), 95–113 (2004)CrossRefGoogle Scholar
  5. 5.
    Vellanki, P., Phung, D., Duong, T., Venkatesh, S.: Learning entry profiles of children with autism from multivariate treatment information using restricted Boltzmann machines. In: Li, X.-L., Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9441, pp. 245–257. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25660-3_21 CrossRefGoogle Scholar
  6. 6.
    Venkatesh, S., Phung, D., Duong, T., Greenhill, S., Adams, B.: TOBY: early intervention in autism through technology. In: Proceedings of the SIGCHI, pp. 3187–3196. ACM (2013)Google Scholar
  7. 7.
    Peters-Scheffer, N., Didden, R., Korzilius, H., Sturmey, P.: A meta-analytic study on the effectiveness of comprehensive aba-based early intervention programs for children with autism spectrum disorders. Res. Autism Spectr. Disord. 5(1), 60–69 (2011)CrossRefGoogle Scholar
  8. 8.
    Garnett, M.S., Attwood, T., Peterson, C., Kelly, A.B.: Autism spectrum conditions among children and adolescents: a new profiling tool. Aust. J. Psychol. 65(4), 206–213 (2013)CrossRefGoogle Scholar
  9. 9.
    Tran, T., Phung, D., Venkatesh, S.: Mixed-variate restricted Boltzmann machines. In: Proceedings of the 3rd ACML, pp. 213–229 (2011)Google Scholar
  10. 10.
    Nguyen, T.D., Tran, T., Phung, D., Venkatesh, S.: Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7818, pp. 123–135. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37453-1_11 CrossRefGoogle Scholar
  11. 11.
    Hinton, G.E., Salakhutdinov, R.: Replicated softmax: an undirected topic model. In: NIPS, pp. 1607–1614 (2009)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Pratibha Vellanki
    • 1
    Email author
  • Thi Duong
    • 1
  • Dinh Phung
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Pattern Recognition and Data AnalyticsDeakin UniversityWaurn PondsAustralia

Personalised recommendations